{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import wave\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "from scipy.signal import convolve  #所有操作需要的库\n",
    "import soundfile as sf\n",
    "import sounddevice as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(540832,)\n",
      "(540921,)\n"
     ]
    }
   ],
   "source": [
    "#=== 测试卷积类型\n",
    "data,sp=sf.read(\"1-2.wav\")\n",
    "print(data.shape)\n",
    "ans = convolve(data,data[10:100])  #卷积操作\n",
    "print(ans.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== 播放音频\n",
    "import time\n",
    "sd.play(data,sp) # 异步\n",
    "time.sleep(1)  # 等待3秒后中断  \n",
    "sd.stop()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 遍历目录下的.wav文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['音频/niluhu\\\\1-1.wav',\n",
       " '音频/niluhu\\\\1.wav',\n",
       " '音频/niluhu\\\\10.wav',\n",
       " '音频/niluhu\\\\2.wav',\n",
       " '音频/niluhu\\\\26.wav',\n",
       " '音频/niluhu\\\\27.wav',\n",
       " '音频/niluhu\\\\28.wav',\n",
       " '音频/niluhu\\\\29.wav',\n",
       " '音频/niluhu\\\\3.wav',\n",
       " '音频/niluhu\\\\30.wav',\n",
       " '音频/niluhu\\\\31.wav',\n",
       " '音频/niluhu\\\\32.wav',\n",
       " '音频/niluhu\\\\33.wav',\n",
       " '音频/niluhu\\\\34.wav',\n",
       " '音频/niluhu\\\\35.wav',\n",
       " '音频/niluhu\\\\36.wav',\n",
       " '音频/niluhu\\\\37.wav',\n",
       " '音频/niluhu\\\\38.wav',\n",
       " '音频/niluhu\\\\39.wav',\n",
       " '音频/niluhu\\\\4.wav',\n",
       " '音频/niluhu\\\\40.wav',\n",
       " '音频/niluhu\\\\41.wav',\n",
       " '音频/niluhu\\\\42.wav',\n",
       " '音频/niluhu\\\\43.wav',\n",
       " '音频/niluhu\\\\44.wav',\n",
       " '音频/niluhu\\\\45.wav',\n",
       " '音频/niluhu\\\\46.wav',\n",
       " '音频/niluhu\\\\47.wav',\n",
       " '音频/niluhu\\\\48.wav',\n",
       " '音频/niluhu\\\\49.wav',\n",
       " '音频/niluhu\\\\5.wav',\n",
       " '音频/niluhu\\\\50.wav',\n",
       " '音频/niluhu\\\\51.wav',\n",
       " '音频/niluhu\\\\52.wav',\n",
       " '音频/niluhu\\\\53.wav',\n",
       " '音频/niluhu\\\\54.wav',\n",
       " '音频/niluhu\\\\55.wav',\n",
       " '音频/niluhu\\\\56.wav',\n",
       " '音频/niluhu\\\\57.wav',\n",
       " '音频/niluhu\\\\58.wav',\n",
       " '音频/niluhu\\\\6.wav',\n",
       " '音频/niluhu\\\\7.wav',\n",
       " '音频/niluhu\\\\8.wav',\n",
       " '音频/niluhu\\\\9.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_friendship2_02b_4.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_friendship3_02a_3.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_friendship3_02b_1.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_friendship3_03.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_hello_01.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_hello_02.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_morning_01.wav',\n",
       " '音频/niluhu\\\\vo_nilou_mimitomo_night_01.wav']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=== 遍历目录下的.wav文件\n",
    "import os\n",
    "def get_file_list(dir)->list:\n",
    "    \"\"\"\n",
    "    遍历目录下的.wav文件\n",
    "\n",
    "    Args:\n",
    "        dir: 目录\n",
    "    \n",
    "    Returns:\n",
    "        file_list: 目录下的.wav文件路径列表\n",
    "    \"\"\"\n",
    "    file_list = []\n",
    "    for root, dirs, files in os.walk(dir):\n",
    "        for file in files:\n",
    "            if os.path.splitext(file)[1] == '.wav':\n",
    "                file_list.append(os.path.join(root, file))\n",
    "    return file_list\n",
    "\n",
    "get_file_list('音频/niluhu')  # 遍历目录下的.wav文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== 将.wav文件拼接成一个文件\n",
    "def wav_concat(file_list:str, output_file:str,blank_fps:int=46000):\n",
    "    \"\"\"\n",
    "    将多个.wav文件拼接成一个文件\n",
    "\n",
    "    Args:\n",
    "        file_list: .wav文件路径列表\n",
    "        output_file: 输出文件路径\n",
    "        blank_fps: 拼接后，每个wav文件之间的空白（过渡）帧数\n",
    "        \n",
    "    \"\"\"\n",
    "\n",
    "    data = []\n",
    "    for file in file_list:\n",
    "        y, sr = sf.read(file)\n",
    "        data.append(y)\n",
    "\n",
    "      \n",
    "        data.append(np.zeros((blank_fps, )))\n",
    "    data = np.concatenate(data, axis=0)\n",
    "    sf.write(output_file, data, sr,format='MP3')\n",
    "\n",
    "\n",
    "wav_concat(get_file_list('音频/niluhu'), '音频/niluhu_blank.mp3')  # 将.wav文件拼接成一个文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 录音"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== 录音\n",
    "myrecording = sd.rec(int(3 * 44100), samplerate=44100, channels=1)\n",
    "sd.wait() \n",
    "sf.write('adc.wav', myrecording, 44100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,)\n",
      "[ 2.02322781e-07  5.37382769e-08 -4.39902342e-06  1.83025948e-06\n",
      " -1.48365641e-04]\n",
      "14\n",
      "[  1   4  10  20  35  50  65  80  95 110 114 106  85  50]\n",
      "[  1   4  10  20  35  50  65  80  95 110 114 106  85  50]\n"
     ]
    }
   ],
   "source": [
    "file_path = 'cipic-hrtf-database-master\\standard_hrir_database\\subject_021\\hrir_final.mat' \n",
    "data = loadmat(file_path)\n",
    "hrir_l = data['hrir_l']\n",
    "hrir_r = data['hrir_r']\n",
    "print(hrir_l[0,0,:].shape)\n",
    "print(hrir_l[0,0,0:5])\n",
    "\n",
    "a = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "b= [1,2,3,4,5]\n",
    "res = np.convolve(a,b)\n",
    "res2 = convolve(a,b)\n",
    "print(len(res))\n",
    "print(res2)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 虚拟环绕算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa \n",
    "def virtual_surround(sound_path:str,result_path:str=None,source_voice=None,samplerate=None,h_angle=0,v_angle=0,slice_size:int=None,slice_mode=\"fade\",conv_list:list=[(24,0)]):\n",
    "    \"\"\"对单声道音频施加虚拟环绕算法，生成立体声音频\n",
    "    当存在多个卷积核时，一段音频会被切成多段，每段对应一个方位卷积\n",
    "\n",
    "    ## 缺陷\n",
    "    分段处理时，段落直接会出现杂音，如何解决\n",
    "\n",
    "    ## 依赖\n",
    "    import numpy as np  \n",
    "\n",
    "    from scipy.io import loadmat  \n",
    "\n",
    "    from scipy.signal import convolve  \n",
    "    \n",
    "    import soundfile as sf  \n",
    "    \n",
    "    ## 参数\n",
    "\n",
    "    sound_path: str 音频文件路径\n",
    "    result_path: str 输出文件路径\n",
    "    source_voice: np.array 单声道音频数据 (xxx,) 建议采样率44100Hz\n",
    "    samplerate: int 音频采样率\n",
    "    h_angle: int 废弃 水平方位角（0~24）\n",
    "    v_angle: int 废弃 高度度（0~49）\n",
    "    slice_size: int 分段大小。 若为None，分段大小由滤波器个数由决定\n",
    "    slice_mode: str 对分段边界的处理方式（fade：淡入淡出,energy：找静音点分割）\n",
    "    conv_list: list 滤波器列表，将会逐一循环作用与每一段音频帧[(h1,v1),(h2,v2),...]\n",
    "    \n",
    "    ## 返回值\n",
    "    np.array\n",
    "        立体声音频数据 (xxx,2)\n",
    "    \"\"\"\n",
    "     # 读取音频\n",
    "    if source_voice is not None:\n",
    "        sound_data = source_voice\n",
    "        sample_rate = samplerate\n",
    "    else:\n",
    "        sound_data, sample_rate = sf.read(sound_path) \n",
    "    \n",
    "    # 调整音频采样率\n",
    "    if sample_rate != 44100:\n",
    "        sound_data = librosa.resample(sound_data, sample_rate, 44100)\n",
    "\n",
    "    # 加载hrtf参数\n",
    "    file_path = 'cipic-hrtf-database-master\\standard_hrir_database\\subject_021\\hrir_final.mat' \n",
    "    data = loadmat(file_path)\n",
    "    hrir_l = data['hrir_l']\n",
    "    hrir_r = data['hrir_r']\n",
    "\n",
    "    processed_list_l = [] # 存储处理后的音频数据\n",
    "    processed_list_r = [] # 存储处理后的音频数据\n",
    "    \n",
    "    fps_num = sound_data.shape[0] # 音频帧数\n",
    "    conv_num = len(conv_list) # 滤波器数量\n",
    "    if slice_size is None:\n",
    "        slice_size = fps_num //conv_num # 动态设置分段大小\n",
    "    \n",
    "    \n",
    "    slice_num = fps_num // slice_size # 分段数\n",
    "    remain_fps = fps_num % slice_size # 剩余帧数\n",
    "    slice_list=[sound_data[i*slice_size:(i+1)*slice_size] for i in range(slice_num)] # 分段音频\n",
    "    \n",
    "    if remain_fps != 0:\n",
    "        slice_list.append(sound_data[-remain_fps:])\n",
    "        slice_num += 1\n",
    "\n",
    "    print(f\"\"\"参数\n",
    "    音频帧数：{fps_num}\n",
    "    滤波器数量：{conv_num}\n",
    "    分段数：{slice_num}\n",
    "    切片大小：{slice_size}\n",
    "          \"\"\")\n",
    "    \n",
    "    fade_length = 2048\n",
    "    fade_out = np.linspace(1., 0., fade_length)\n",
    "    fade_in = np.linspace(0., 1., fade_length)\n",
    "\n",
    "    for i in range(slice_num):# 对每一段音频使用不同的滤波器卷积\n",
    "        slice_data = slice_list[i] # 音频分段\n",
    "        slice_data[:fade_length] = slice_data[:fade_length:]*fade_in\n",
    "        slice_data[-fade_length:] = slice_data[-fade_length:]*fade_out\n",
    "        h_angle = conv_list[i%conv_num][0] \n",
    "        v_angle = conv_list[i%conv_num][1]\n",
    "        hrir_a = hrir_l[h_angle,v_angle,:]   # 左耳滤波器\n",
    "        hrir_b = hrir_r[h_angle,v_angle,:]   # 右耳滤波器\n",
    "        proc_data_l = convolve(slice_data,hrir_a,mode='same') # 左耳卷积\n",
    "        processed_list_l.append(proc_data_l)\n",
    "        proc_data_r = convolve(slice_data,hrir_b,mode='same') # 右耳卷积\n",
    "        processed_list_r.append(proc_data_r)\n",
    "    processed_list_l = np.concatenate(processed_list_l,axis=0)\n",
    "    processed_list_r = np.concatenate(processed_list_r,axis=0)\n",
    "    stereo = np.vstack((processed_list_l,processed_list_r)).T\n",
    "\n",
    "    if result_path is not None:\n",
    "        sf.write(result_path, stereo, sample_rate)\n",
    "    \n",
    "    return stereo\n",
    "    \n",
    "   \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数\n",
      "    音频帧数：316580\n",
      "    滤波器数量：1\n",
      "    分段数：1\n",
      "    切片大小：316580\n",
      "          \n",
      "参数\n",
      "    音频帧数：316580\n",
      "    滤波器数量：1\n",
      "    分段数：1\n",
      "    切片大小：316580\n",
      "          \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.02675116e-04, 1.41026653e-04],\n",
       "       [1.37556659e-04, 1.32051413e-04],\n",
       "       [7.32223888e-05, 1.17015365e-04],\n",
       "       ...,\n",
       "       [0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=== 执行算法\n",
    "virtual_surround('音频/niluhu/1-1.wav','1-1.wav',conv_list=[(12,40)])\n",
    "virtual_surround('音频/niluhu/1-1.wav','1-1_10000.wav',conv_list=[(12,10)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数\n",
      "    音频帧数：13842868\n",
      "    滤波器数量：27\n",
      "    分段数：70\n",
      "    切片大小：200000\n",
      "          \n"
     ]
    }
   ],
   "source": [
    "path_list=get_file_list('音频/niluhu') \n",
    "hv_list_1 = [(0,0),(3,0),(6,0),(12,0),(18,0),(24,0),(20,48),(14,48),(8,48),(4,48),(0,48)] # 下方环绕\n",
    "hv_list_2=[(0,24),(6,24),(12,24),(18,24),(24,24),(20,24),(10,24)] # 顶部环绕\n",
    "hv_list_3=[(0,8),(6,8),(12,8),(18,8),(24,8),(20,40),(14,40),(8,40),(2,40)] # 正前后方环绕\n",
    "wav_concat(path_list,'result/niluhu.wav')\n",
    "res = virtual_surround('result/niluhu.wav',slice_size=200000,conv_list=hv_list_1+hv_list_2+hv_list_3)\n",
    "sf.write('result/niluhu_surround_test.wav',res,samplerate=48000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316580,) 44100\n",
      "(224192,) 48000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\impossible\\AppData\\Local\\Temp\\ipykernel_18768\\199667028.py:42: UserWarning: Glyph 38899 (\\N{CJK UNIFIED IDEOGRAPH-97F3}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\impossible\\AppData\\Local\\Temp\\ipykernel_18768\\199667028.py:42: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=== 绘制波形图\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "\n",
    "# 定义要读取的音频文件路径列表\n",
    "audio_files = [\n",
    "    '音频/niluhu/1-1.wav',  # 替换为你的第一个音频文件路径\n",
    "    '音频/niluhu/9.wav',  # 替换为你的第二个音频文件路径\n",
    "\n",
    "    # 可以继续添加更多的音频文件路径\n",
    "]\n",
    "\n",
    "# 创建一个新的图形和子图布局\n",
    "fig, axs = plt.subplots(len(audio_files), 1, figsize=(14, 5 * len(audio_files)))\n",
    "\n",
    "# 如果只有一个音频文件，则 axs 不会作为一个列表返回，所以需要处理这种情况\n",
    "if len(audio_files) == 1:\n",
    "    axs = [axs]\n",
    "\n",
    "for ax, audio_path in zip(axs, audio_files):\n",
    "    # 读取音频文件\n",
    "    data, samplerate = sf.read(audio_path)\n",
    "    print(data.shape,samplerate)\n",
    "\n",
    "    # 处理多声道音频（如果有的话），这里只绘制第一个声道\n",
    "    if len(data.shape) > 1:\n",
    "        data = data[:, 0]  # 取第一个声道作为示例\n",
    "    \n",
    "    # 创建时间轴\n",
    "    duration = len(data) / samplerate\n",
    "    time = np.linspace(0, duration, len(data))\n",
    "\n",
    "    # 绘制波形图\n",
    "    ax.plot(time, data)\n",
    "    ax.set_title(f'Waveform - {audio_path}')\n",
    "    ax.set_xlabel('Time [s]')\n",
    "    ax.set_ylabel('Amplitude')\n",
    "\n",
    "# 调整布局防止重叠\n",
    "plt.tight_layout()\n",
    "\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取音频"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316580,)\n",
      "44100\n",
      "<class 'numpy.ndarray'>\n",
      "19287\n"
     ]
    }
   ],
   "source": [
    "import soundfile as sf  \n",
    "  \n",
    "# 加载音频文件  \n",
    "audio_data, sample_rate = sf.read('音频/niluhu/1-1.wav')  \n",
    "  \n",
    "# 获取声道数  \n",
    "print(audio_data.shape)\n",
    "print(sample_rate)\n",
    "print(type(audio_data))\n",
    "\n",
    "# 连续0的个数\n",
    "def count_zero(data):\n",
    "    count = 0\n",
    "    for i in data:\n",
    "        if i == 0:\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "print(count_zero(audio_data))\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316580,)\n",
      "44100\n"
     ]
    }
   ],
   "source": [
    "import librosa  \n",
    "# 加载音频文件  \n",
    "y, sr = librosa.load('音频/niluhu/1.wav', mono=False,sr=44100)  \n",
    "# 获取声道数  \n",
    "print(y.shape)\n",
    "print(sr)\n",
    "# 保存音频文件\n",
    "sf.write('音频/niluhu/1-1.wav', y, sr)\n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单双声道转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(344576,) (176256,)\n",
      "(170000, 2)\n"
     ]
    }
   ],
   "source": [
    "audio_data, sample_rate = sf.read('音频/niluhu/1.wav') # 将用于左声道的音频\n",
    "audio_data2, sample_rate2 = sf.read('音频/niluhu/2.wav') # 将用于右声道的音频\n",
    "print(audio_data.shape, audio_data2.shape)\n",
    "\n",
    "stereo_signal = np.stack((audio_data[:170000], audio_data2[:170000]), axis=1) \n",
    "print(stereo_signal.shape) \n",
    "\n",
    "sf.write('stereo_audio.wav', stereo_signal, sample_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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